The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Exploration, mining, and analysis of high-volume data sources and data warehouses can be a time and resource intensive process, both for the user and for the underlying computing systems which provide data analysis features. Large volumes of data contribute to high consumption of computer memory, and processing of such large volumes of data can require extremely powerful computer processors and processing time. Users such as data analysts often face a seemingly insurmountable task of attempting to locate discrete pieces of information from data sources which can include or primarily consist of innocuous or noisy data which provides little probative value. Often, many iterations to explore such data are required, with each iteration having an associated cost in time and resources.